1. Cross Reference to Microfiche Appendix
This application includes a plurality of computer program listings (modules) in the form of a Microfiche Appendix which is being filed concurrently herewith as 1162 frames (not counting target and title frames) distributed over 20 sheets of microfiche in accordance with 37 C.F.R. § 1.96. The disclosed computer program listings are incorporated into this specification by reference but it should be noted that the source code and/or the resultant object code of the disclosed program modules are subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document (or the patent disclosure as it appears in the files or records of the U.S. Patent and Trademark Office) for the sole purpose of studying the disclosure but otherwise reserves all other rights to the disclosed computer program modules including the right to reproduce said computer program modules in machine-executable form.
2. Field of Invention
The present invention relates generally to computer database management systems and more specifically to apparatus and methods for modifying and searching through large scale databases at high speed.
3. Description of Related Art
Modern computer systems are capable of storing voluminous amounts of information in bulk storage means such as magnetic disk banks. The volume of stored information can be many times that of the textual information stored in a conventional encyclopedia or in the telephone directory of a large city. Moreover, modern computer systems can sift through the contents of their bulk storage means at extremely high speed, accessing as many as one million bytes of information or more per second (a byte is a string of eight bits, equivalent to approximately one character of text in layman's terms). Despite this capability, it may take an undesirably long time (i.e., hours or days) to retrieve desired pieces of information. In commercial settings such as financial data storage facilities, there will be literally, billions of pieces of information that could be sifted through before the right one or more pieces of information are found. Thus, even at speeds of one million examinations per second, it can take thousands of seconds (many hours) to retrieve a desired piece of information. Efficient organization of the stored information is needed in order to minimize retrieval time.
The methods by which pieces of information are organized within a computer, searched through or reorganized, often parallel techniques used by older types of manual information processing systems. A well known example of a manual system is the index card catalog found in public libraries. Such a card catalog consists of a large number of uniformly dimensioned paper cards which are serially stacked in one or more trays. The cards are physically positioned such that each card is directly adjacent to no more than two others (for each typical examination there is a preceding card, the card under examination and a following card in the stack). On the front surface of each index card a librarian enters, in left to right sequence; the last name of an author, the first name of the author, the title of a single book which the author wrote and a shelf number indicating the physical location within the library where the one book may be found. Each of these four entries may be referred to as a “column” entry. Sufficient surface area must be available on each card to contain the largest of conceivable entries.
After the entries are made, the index cards are stacked one after the next in alphabetical order, according to the author's last name and then according to the author's first name and then by title. This defines a “key-sequenced” type of database whose primary sort key is the author's name. The examination position of each card is defined relative to the contents of preceding and following cards in the stack. That is, when cards are examined, each intermediate card is examined immediately after its alphabetically preceding card and immediately before its alphabetically succeeding card. When a new book is acquired, the key-sequenced database is easily “updated” by inserting a new card between two previously created cards. Similarly, if a book is removed from the collection, its card is simply pulled from the card stack to reflect the change.
If a library user has an inquiry respecting the location of a particular book or the titles of several books written by a named author, the librarian may quickly search through the alphabetically ordered set of index cards and retrieve the requested information. However, if a library user has an inquiry which is not keyed to an author's name, the search and retrieval process can require substantially more time; the worst case scenario being that for each inquiry the librarian has to physically sift through and examine each card in the entire catalog. As an example of such a scenario, suppose that an inquiring reader asks for all books in the library where the author's first name is John and the title of the book contains the word “neighbor” or a synonym thereof. Although it is conceptually possible to answer this inquiry using the information within the catalog, the time for such a search may be impractically long, and hence, while the information is theoretically available, it is not realistically accessible.
To handle the more common types of inquiries, libraries often keep redundant sets of index cards. One set of cards is sorted according to author names and another set is sorted according to the subject matter of each book. This form of redundant storage is disadvantageous because the size of the card catalog is doubled and hence, the cost of information storage is doubled. Also, because two index cards must be generated for each new book added to the collection the cost of tipdating the catalog is also doubled.
The size of a library collection tends to grow over time as more and more books are acquired. During the same time, more and more index cards are added to the catalog. The resulting stack of cards, which may be viewed as a kind of “database”, therefore grows both in size and in worth. The “worth” of the card-based system may be defined in part as the accumulated cost of all work that is expended in creating each new index card and in inserting the card into an appropriate spot in the stack.
As time goes by, not only does the worth and size of the database grow, but new technologies, new rules, new services, etc., begin to emerge and the information requirements placed on the system change. Some of these changes may call for a radical reorganization of the card catalog system. In such cases, a great deal of work previously expended to create the catalog system may have to be discarded and replaced with new work.
For the sake of example, let it be supposed that the library acquires a new microfilm machine which stores copies of a large number of autobiographies. The autobiographies discuss the life and literary works of many authors whose books are kept in the library. Let it further be supposed that the original, first card catalog system is now required to cross reference each book to the microfilm location (or plural locations) of its author's (or plural authors') autobiographies. In such a case, the card catalog system needs to be modified by adding at least one additional column of information to each index card to indicate the microfilm storage locations of the relevant one or more autobiographies.
We will assume here that there is not enough surface area available on the current index cards for adding the new information. Larger cards are therefore purchased, the information from the old cards is copied to the new cards, and finally, the new microfilm cross referencing information is added to the larger cards. This type of activity will be referred to here as “restructuring” the database.
Now let us suppose, that as more time goes by, an additional but previously unanticipated, cross indexing category is required because of the introduction of a newer technology or a new government regulation. It might be that the just revised and enlarged second card system does not have the capacity to handle the demands of the newer technology or regulation. In such a situation, a third card system has to be constructed from scratch. The value of work put into the creation of the just-revised second system is lost. As more time passes and further changes emerge in technology, regulations, etc., it is possible that more major organizational changes will have to be made to the catalog system. Time after time, a system will be built up only to be later scrapped because it fails to anticipate a new type of information storage and retrieval operation. This is quite wasteful.
Although computerized database systems are in many ways different from manual systems, the computerized information storage and retrieval systems of the prior art are analogous to manual systems in that the computerized databases require similar restructuring every time a new category of information relationships or a new type of inquiry is created.
At a fundamental level, separate pieces of information are stored within a computerized database system as a large number of relatively short strings of binary bits where each string has finite length. The bit strings are distributed spacially within a tangible medium of data storage such as an array of magnetic disks, optical devices or other information representing means capable of providing mass storage. Each bit is represented by a magnetic flux reversal, an optical perturbation and/or some other variance in the physical attributes of a data storage medium. A transducer or amplifier means converts these variances into signals (e.g., electrical, magnetic, or optical) which can be processed on a digital data processing machine. Each string of bits is often uniquely identified by its physical location or by a logical storage address. Some bit strings may function as address pointers, rather than as the final pieces of “real” information which a database user wishes to obtain. The address pointers are used to create so-called “threaded list” organizations of data wherein logical links between a first informational “object” (first piece of real data) and a second informational “object” (second piece of real data) are established by a chain of direct or indirect address pointers. The user-desired objects of real information themselves can be represented by a collection of one or more physically or logically connected strings.
Typically, “tables” of information are created within the mass storage means of the computerized system. A horizontal “row” of related objects, which is analogous to a single card in a card catalog system, may be defined by placing the corresponding bit strings of the objects in physical or address proximity with each other. Logical interconnections may be defined between different rows by using ancillary pointers (which are not considered here as the “real” data sought by a database user). A serial sequence of “rows” (analogous to a stack of cards) is then defined by linking one row to another according to a predefined sorting algorithm using threaded list techniques.
A vast number of different linking “threads” may be defined in this way through a database table having millions or billions of binary information bits. Unlike manual systems, the same collection of rows (which replaces the manual stack of cards) can be simultaneously ordered in many different ways by utilizing a multiplicity of threaded paths so that redundant data storage is not necessary. Searches and updates may be performed by following a prespecified thread from one row to the next until a sought piece of information (or its address) is found within a table. A threaded-list type of table can be “updated” in a manner similar to manual card systems by breaking open a logical thread within the list, at a desired point, and inserting a new row (card) or removing an obsolete row at the opened spot.
Tables are often constructed according to a “key-sequenced” approach. One column of a threaded-list table is designated as the sort-key column and the entries in that column are designated as “sort keys”. Address pointers are used to link one row of the table to another row according to a predefined sequencing algorithm which orders the entries (sort-keys) of the sort column as desired (i.e., alphabetically, numerically or otherwise). Once a table is so sorted according to the entries of its sort column, it becomes a simple task to search down the sort column looking for an alphabetically, numerically or otherwise ordered piece of data. Other pieces of data which are located within the row of each sort key can then be examined in the same sequence that each sort key is examined. Any column can serve as the sort column and its entries as the sort keys. Thus a table having a large plurality of columns can be sorted according to a large number of sorting algorithms.
The key-sequencing method gives tremendous flexibility to a computerized database but not without a price. Each access to the memory location of a list-threading address pointer or to the memory location of a sort-key or to the memory area of “real” data which is located adjacent to a sort-key takes time. As more and more accesses are required to fetch pointers and keys leading to the memory location of a piece of sought-after information (“real data”), the response time to an inquiry increases and system performance suffers.
There is certain class of computerized databases which are referred to as “relational databases”. Such database systems normally use threaded list techniques to define a plurality of key-sequenced “tables”. Each table contains at least two columns. One column serves as the sort column while a second or further columns of the table store either the real data that is being sought or additional sort-key data which will ultimately lead to a sought-after piece of real data. The rows of the table are examined in an ordered fashion according to the contents of the sort column. Target data is located by first threading down the sort column and thus moving through the chain of rows within a table according to a prespecified sort algorithm until a specific sort-key is found. Then the corresponding row is examined horizontally and the target data (real data, or the next key) is extracted from that row.
An example of “real” data would be the full-legal names of unique persons such as in the character strings, “Mr. Harry W. Jones”, “Mrs. Barbara R. Smith”, etc. The sort-key can be a number which is stored adjacent to the full name and which sequences the names (real data) according to any of a wide variety of ordering patterns including by age, by height, by residential address, alphabetically, etc. Because the real data (e.g., full name of a person) is stored in a separate column, it is independent from the sort key data. A large variety of different relations can therefore be established between a first piece of real data (e.g., a first person's name) and a second piece of real data (e.g., a second person's name) simply by changing the sort keys that are stored in the separate sort column (e.g., who is older than whom, who is taller, etc.). Plural orderings of the real data can be obtained at one time by providing many columns in one table, by storing alternate keys in the columns and by choosing one or more of these columns as the primary sort key column.
Relational database systems often include tables that do not store real data in a column adjacent to their sort-key column, but rather store a secondary key number which directs a searcher to a row in another key-sequenced table where a matching key number is held together with either a piece of sought-after real data or yet another forward referencing key number (e.g., an entry which in effect says “find the row which holds key number x of yet another table for further details”). With this indirect key-sequenced approach, a large number of tables can be simultaneously updated by changing one entry in a “base” table.
Relational database tables are normally organized to create implied set and subset “relations” between their respective items of pre-stored information. The elements of the lowest level subsets are stored in base tables and higher level sets are built by defining, in other tables, combinations of keys which point to the base tables. The implied relations between elements cannot be discerned by simply inspecting the raw data of each table. Instead, relations are flushed out only with the aid of an access control program which determines in its randomly-distributed object code, which table to examine first and what column to look at before beginning to search down the table's column for a key number and, when that key number is found, what other column to look at for the real data or a next key number. Relations between various “entities” of a relational database are implied by the sequence in which the computer accesses them.
By way of a concrete example, consider a first relational table (Names-Table) which lists the names of a large number of people in telephone directory style. Each name (each separate item of real data) is paired to a unique key number and the rows of this Names-Table are sorted sequentially according to the key number. A second relational table may be provided in-the database (Cars-Table) which lists automobile (vehicle) identification numbers (VIN) each paired in its row with a second key number. If the second key number is matched by a corresponding key number in the first table, then a relationship might be implied between the entries of the two separate tables (Names-Table and Cars-Table). The “implied” relationship might be one of an infinite set of possibilities. The relationship could be, for example, that the car listed in the second table is “owned” by the person whose name is found next to a matching key in the first table. On the other hand, it might be implied that the matched person in the first table “drives” the car, or “cleans” the car or has some other relation to the car. It is left to the access control program to define what the relationship is between entities in the first table and entities in the second table.
It can be seen that relational database systems offer users a great deal of flexibility since an infinite number of relations may be defined (implied). Economy in maintaining (updating) the database is also provided since a change to a base table propagates through all other tables which reference the base table. The access control program of the database system can include information-updating modules which, for example, change the key number in the second table (Cars-Table) whenever ownership of a car changes. If the name of the new owner is already in the first table (Names-Table), it does not have to be typed a second time into a new storage area and thus, extra work and storage redundancy are avoided. The vehicle identification number (VIN) remains unchanged. Minimal work is thus expended on updating the database.
Despite these advantages, relational database systems suffer from expandability and restructuring problems similar to those of the above-described manual system. Sometimes the rows within a particular table have to be altered to add additional columns. This is not easily done. Suppose for example, that a new government regulation came into being, mandating that vehicles are to always be identified not only by a vehicle identification number (VIN) but also by the name and location of the factory where the vehicle was assembled. If spare columns are not available in the Cars-Table, the entire database may have to be restructured to create extra room in the storage means (i.e. the disk bank) for adding the newly required columns. New key numbers will have to be entered into the new columns of each row (e.g., a new “factory of assembly” key number) and sorted in order to comply with the newly mandated regulation. New search and inquiry routines will have to be written for handling the newly structured tables.
In the past, much of this restructuring work was done by reprogramming the computer at the object code or source code level. This process relied heavily on an expert programming staff. It was time consuming, costly and prone to programming errors. Worst of all, it had to be redone time and again as new informational requirements emerged just after a last restructuring project was completed. There is a need in the industry for a database management system which provides quick responses to inquiries and which can also be continuously updated or restructured without reprogramming at the source or object code level.